Metformin shows anti‐inflammatory effects in murine macrophages through Dicer/microribonucleic acid‐34a‐5p and microribonucleic acid‐125b‐5p
Bibliographic record
Abstract
AIMS/INTRODUCTION: Metformin, a widely prescribed antidiabetic agent, has been shown to exhibit anti-inflammatory effects in obese and type 2 diabetes patients, but the mechanism is not well elucidated. Microribonucleic acids (miRNAs) are a group of small non-coding ribonucleic acids that participate in many biological and pathological processes. The aim of the present study was to investigate whether Dicer, a key miRNA biogenesis enzyme, and miRNAs in macrophages are implicated in the anti-inflammatory effects of metformin. MATERIALS AND METHODS: Enzyme-linked immunosorbent assay and reverse transcription quantitative polymerase chain reaction were carried out to verify the anti-inflammatory effects of metformin. miRNA microarray was applied to detect the expression profile of miRNA. Western-blotting, enzyme-linked immunosorbent assay and reverse transcription quantitative polymerase chain reaction were used to examine the role Dicer and miRNAs play in the anti-inflammatory effects of metformin. RESULTS: In parallel with the suppression of interleukin-6 and tumor necrosis factor-α production in resting and lipopolysaccharide-stimulated macrophages, metformin could induce an increase in Dicer and most miRNAs. When Dicer was knocked down, the anti-inflammatory effects of metformin were significantly attenuated. Additionally, the upregulation of miRNA (miR)-34a-5p and miR-125b-5p by metformin were also blunted in Dicer knockdown macrophages. Furthermore, inhibition of miR-34a-5p and miR-125b-5p could impair the suppressive action of metformin on pro-inflammatory factors production, whereas overexpression of the two miRNAs mimicked the anti-inflammatory effects of metformin. CONCLUSIONS: Metformin might show anti-inflammatory effects in macrophages through the induction of Dicer and the subsequent upregulation of miR-34a-5p and miR-125b-5p.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".